粒子群優(yōu)化算法（PSO）來源于鳥類的群體行為,是一種基于種群的隨機(jī)搜索算法。粒子群優(yōu)化算法的性能在很大程度上取決于參數(shù)的選擇。其中,慣性權(quán)重是該算法的參數(shù)之一,用于平衡粒子群優(yōu)化算法的探測和開發(fā)能力。為了提升算法的性能,PSO應(yīng)較好地兼顧全局搜索能力與局部搜索能力。PSO算法具有收斂速度快、易于實現(xiàn)、參數(shù)調(diào)整少等優(yōu)點(diǎn),其有效的搜索策略使之具有潛力在各種應(yīng)用中解決不同優(yōu)化問題的方法。PSO在電力系統(tǒng)優(yōu)化、過程控制、動態(tài)優(yōu)化、自適應(yīng)控制和電磁優(yōu)化等領(lǐng)域有著廣泛的應(yīng)用。雖然PSO在求解許多優(yōu)化問題時表現(xiàn)出良好的性能,但與大多數(shù)隨機(jī)搜索方法一樣,它也存在早熟收斂問題,尤其是在多模態(tài)優(yōu)化問題中。雖然取得了顯著的進(jìn)展和豐碩的成果,但如何較好地平衡粒子群優(yōu)化算法的探測和開發(fā)能力以確定復(fù)雜優(yōu)化問題的高質(zhì)量解決方案,仍然具有一定的挑戰(zhàn)性。本文將局部搜索與粒子群算法相結(jié)合,以平衡算法的探測與開發(fā)能力,并將改進(jìn)后的方法應(yīng)用于許多優(yōu)化問題和實際問題。本文的主要工作如下:（1）為了解決這一問題,本文提出了一種基于梯度局部搜索策略的自適應(yīng)慣性權(quán)重粒子群優(yōu)化算法（SIW-APSO-LS）。該算法旨在平衡全局搜索算法... 

【文章來源】：江蘇大學(xué)江蘇省

【文章頁數(shù)】：116 頁

【學(xué)位級別】：博士

【文章目錄】：
DEDICATION
Abstract
中文摘要
Chapter1 Introduction
    1.1 Background of particle swarm optimization
        1.1.1 Research significance of PSO
        1.1.2 Review and analysis of hybrid PSO
    1.2 Analysis and significance of study
    1.3 Main work and novelty
    1.4 The organization of thesis
Chapter2 Preliminaries
    2.1 Inertia weight in PSO
        2.1.1 Constant inertia weight
        2.1.2 Time-varying inertia weight strategies
        2.1.3 Adaptive inertia weight
    2.2 Dynamic multi swarm particle swarm optimization
    2.3 Gravitation search algorithm
    2.4 Conclusions
Chapter3 An improved self-hybrid inertia weight adaptive particle swarm optimization with local search
    3.1 Introduction
        3.1.1 Gradient based local search(BFGS)algorithm
    3.2 The proposed hybrid algorithm
        3.2.1 The self-hybrid inertia weight adaptive particle swarm optimization
        3.2.2 Framework of SIW-APSO-LS
    3.3 Experimental results and discussion
        3.3.1 Static test functions
        3.3.2 Moving peaks benchmark
        3.3.3 CEC13 test suit
        3.3.4 Comparison with inertia weight adjusting techniques
        3.3.5 Comparison of different methods through moving peaks benchmark(MPB)
        3.3.6 CEC13 test functions
        3.3.7 Comparison of the proposed algorithm(SIW-APSO-LS)with other PSO variants
        3.3.8 Computational cost
        3.3.9 Real world optimization problem
    3.4 Conclusions
Chapter4 Feature selection based SIW-APSO-LS and C4.5 decision tree classifier
    4.1 Introduction
        4.1.1 Preliminary feature selection methodology
        4.1.2 C4.5 decision tree classifier
    4.2 The proposed feature selection method
    4.3 Experimental results and discussions
        4.3.1 Datasets
        4.3.2 Parameters setting
        4.3.3 Evaluation criteria
        4.3.4 Comparison of proposed algorithm to other algorithms
    4.4 Conclusions
Chapter5 Hybrid gravitational search algorithm with dynamic multi swarm particle swarm optimization
    5.1 Introduction
    5.2 The proposed hybrid PSO based on GSA
    5.3 Experimental study and discussion
        5.3.1 Static test functions
        5.3.2 CEC13 test suit
        5.3.3 Moving peaks benchmark
        5.3.4 Comparison with other techniques
        5.3.5 Computational cost of different algorithms
        5.3.6 Comparison through CEC13 test functions
        5.3.7 Comparison of different methods through MPB
        5.3.8 Tension and compression spring design
    5.4 Conclusions
Chapter6 Training a feedforward neural networks using GSADMSPSO
    6.1 Introduction
        6.1.1 Feed-forward neural network and multi-layer perceptron
    6.2 The proposed GSADMSPSO algorithm
        6.2.1 GSADMSPSO method for training FNNs
    6.3 Results and discussions
        6.3.1 The N bits parity(XOR)problem
        6.3.2 Comparison through three bits parity problem(3-bit XOR)
        6.3.3 Comparison with other techniques through standard classification datasets
    6.4 Conclusions
Chapter7 Conclusions and future Work
    7.1 Conclusions
    7.2 Future work
References
Acknowledgements
Publications and participating research fundings



本文編號：3557200